scholarly journals Visualization of multidimensional shape and texture features in laser range data using complex-valued Gabor wavelets

1995 ◽  
Vol 1 (1) ◽  
pp. 44-59 ◽  
Author(s):  
M.H. Gross ◽  
R. Koch
Keyword(s):  
Texture Features ◽  
Gabor Wavelets ◽  
Range Data ◽  
Laser Range ◽  
Complex Valued

On filtering and compressing lageos laser range data

1983 ◽  
Vol 57 (1-4) ◽  
pp. 121-130 ◽  
Author(s):  
E. G. Masters ◽  
A. Stolz ◽  
B. Hirsch
Keyword(s):  
Range Data ◽  
Laser Range

Generic Distance-Invariant Features for Detecting People with Walking Aid in 2D Laser Range Data

2015 ◽  
pp. 735-747 ◽  
Author(s):  
Christoph Weinrich ◽  
Tim Wengefeld ◽  
Michael Volkhardt ◽  
Andrea Scheidig ◽  
Horst-Michael Gross
Keyword(s):  
Range Data ◽  
Laser Range ◽  
Invariant Features ◽  
Walking Aid

MULTIPLE-TARGET CLASSIFICATION AND TRACKING FOR MOBILE ROBOTS USING A 2D LASER RANGE SCANNER

2012 ◽  
Vol 09 (04) ◽  
pp. 1250025 ◽  
Author(s):  
POLYCHRONIS KONDAXAKIS ◽  
HARIS BALTZAKIS
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Moving Objects ◽  
Data Association ◽  
Human Robot Interaction ◽  
Range Data ◽  
Multiple Model ◽  
Target Classification ◽  
Laser Range ◽  
Fuzzy Art

In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.

Identifying rectangles in laser range data for urban scene reconstruction

2011 ◽  
Vol 35 (3) ◽  
pp. 719-725 ◽  
Author(s):  
Thijs van Lankveld ◽  
Marc van Kreveld ◽  
Remco Veltkamp
Keyword(s):  
Scene Reconstruction ◽  
Range Data ◽  
Laser Range ◽  
Urban Scene
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